Favorable Antiviral Effect of Metformin on Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load in a Randomized, Placebo-Controlled Clinical Trial of Coronavirus Disease 2019.

BACKGROUND: Metformin has antiviral activity against RNA viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The mechanism appears to be suppression of protein translation via targeting the host mechanistic target of rapamycin pathway. In the COVID-OUT randomized trial for outpatient coronavirus disease 2019 (COVID-19), metformin reduced the odds of hospitalizations/death through 28 days by 58%, of emergency department visits/hospitalizations/death through 14 days by 42%, and of long COVID through 10 months by 42%. METHODS: COVID-OUT was a 2 × 3 randomized, placebo-controlled, double-blind trial that assessed metformin, fluvoxamine, and ivermectin; 999 participants self-collected anterior nasal swabs on day 1 (n = 945), day 5 (n = 871), and day 10 (n = 775). Viral load was quantified using reverse-transcription quantitative polymerase chain reaction. RESULTS: The mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (-0.56 log10 copies/mL; 95% confidence interval [CI], -1.05 to -.06; P = .027). Those who received metformin were less likely to have a detectable viral load than placebo at day 5 or day 10 (odds ratio [OR], 0.72; 95% CI, .55 to .94). Viral rebound, defined as a higher viral load at day 10 than day 5, was less frequent with metformin (3.28%) than placebo (5.95%; OR, 0.68; 95% CI, .36 to 1.29). The metformin effect was consistent across subgroups and increased over time. Neither ivermectin nor fluvoxamine showed effect over placebo. CONCLUSIONS: In this randomized, placebo-controlled trial of outpatient treatment of SARS-CoV-2, metformin significantly reduced SARS-CoV-2 viral load, which may explain the clinical benefits in this trial. Metformin is pleiotropic with other actions that are relevant to COVID-19 pathophysiology. CLINICAL TRIALS REGISTRATION: NCT04510194.

Disparities in Patient Portal Use Among Adults With Chronic Conditions.

Importance: Disparities in patient access and use of health care portals have been documented. Limited research has evaluated disparities in portal use during and after the COVID-19 pandemic. Objective: To assess prevalence of health care portal use before, during, and after the most restrictive phase of the pandemic (2019-2022) among the COVID-19 & Chronic Conditions (C3) cohort and to investigate any disparities in use by sociodemographic factors. Design, Setting, and Participants: This cohort study uses data from the C3 study, an ongoing, longitudinal, telephone-based survey of participants with multiple chronic conditions. Participants were middle aged and older-adult primary care patients who had an active portal account, recruited from a single academic medical center in Chicago, Illinois, between 2019 and 2022. Data were analyzed between March and June 2022. Main Outcomes and Measures: Outcomes of portal use (ie, number of days of portal login by year) were recorded for all study participants by the electronic data warehouse. All parent studies had uniform sociodemographic data and measures of social support, self-efficacy, health literacy, and health activation. Results: Of 536 participants (mean [SD] age, 66.7 [12.0] years; 336 [62.7%] female), 44 (8.2%) were Hispanic or Latinx, 142 (26.5%) were non-Hispanic Black, 322 (60.1%) were non-Hispanic White, and 20 individuals (3.7%) identified as other race, including Asian, Native American or Alaskan Native, and self-reported other race. In multivariable analyses, portal login activity was higher during the 3 years of the COVID-19 pandemic compared with the 2019 baseline. Higher portal login activity was associated with adequate health literacy (incidence rate ratio [IRR], 1.51; 95% CI, 1.18-1.94) and multimorbidity (IRR, 1.38; 95% CI, 1.17-1.64). Lower portal activity was associated with older age (≥70 years: IRR, 0.69; 95% CI, 0.55-0.85) and female sex (IRR, 0.77; 95% CI, 0.66-0.91). Compared with non-Hispanic White patients, lower portal activity was observed among Hispanic or Latinx patients (IRR, 0.66; 95% CI, 0.49-0.89), non-Hispanic Black patients (IRR, 0.68; 95% CI, 0.56-0.83), and patients who identified as other race (IRR, 0.42; 95% CI, 0.28-0.64). Conclusions and Relevance: This cohort study using data from the C3 study identified changes in portal use over time and highlighted populations that had lower access to health information. The COVID-19 pandemic was associated with an increase in portal use. Sociodemographic disparities by sex and age were reduced, although disparities by health literacy widened. A brief validated health literacy measure may serve as a useful digital literacy screening tool to identify patients who need further support.

Promoting REproductive Planning And REadiness in Diabetes (PREPARED) Study protocol: a clinic-randomised controlled trial testing a technology-based strategy to promote preconception care for women with type 2 diabetes.

INTRODUCTION: Women with type 2 diabetes (T2DM) are more likely to experience adverse reproductive outcomes, yet preconception care can significantly reduce these risks. For women with T2DM, preconception care includes reproductive planning and patient education on: (1) the importance of achieving glycaemic control before pregnancy, (2) using effective contraception until pregnancy is desired, (3) discontinuing teratogenic medications if pregnancy could occur, (4) taking folic acid, and (5) managing cardiovascular and other risks. Despite its importance, few women with T2DM receive recommended preconception care. METHODS AND ANALYSIS: We are conducting a two-arm, clinic-randomised trial at 51 primary care practices in Chicago, Illinois to evaluate a technology-based strategy to 'hardwire' preconception care for women of reproductive age with T2DM (the PREPARED (Promoting REproductive Planning And REadiness in Diabetes) strategy) versus usual care. PREPARED leverages electronic health record (EHR) technology before and during primary care visits to: (1) promote medication safety, (2) prompt preconception counselling and reproductive planning, and (3) deliver patient-friendly educational tools to reinforce counselling. Post-visit, text messaging is used to: (4) encourage healthy lifestyle behaviours. English and Spanish-speaking women, aged 18-44 years, with T2DM will be enrolled (N=840; n=420 per arm) and will receive either PREPARED or usual care based on their clinic's assignment. Data will be collected from patient interviews and the EHR. Outcomes include haemoglobin A1c (primary), reproductive knowledge and self-management behaviours. We will use generalised linear mixed-effects models (GLMMs) to evaluate the impact of PREPARED on these outcomes. GLMMs will include a fixed effect for treatment assignment (PREPARED vs usual care) and random clinic effects. ETHICS AND DISSEMINATION: This study was approved by the Northwestern University Institutional Review Board (STU00214604). Study results will be published in journals with summaries shared online and with participants upon request. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04976881).

Strategies used for the COVID-OUT decentralized trial of outpatient treatment of SARS-CoV-2.

The COVID-19 pandemic accelerated the development of decentralized clinical trials (DCT). DCT's are an important and pragmatic method for assessing health outcomes yet comprise only a minority of clinical trials, and few published methodologies exist. In this report, we detail the operational components of COVID-OUT, a decentralized, multicenter, quadruple-blinded, randomized trial that rapidly delivered study drugs nation-wide. The trial examined three medications (metformin, ivermectin, and fluvoxamine) as outpatient treatment of SARS-CoV-2 for their effectiveness in preventing severe or long COVID-19. Decentralized strategies included HIPAA-compliant electronic screening and consenting, prepacking investigational product to accelerate delivery after randomization, and remotely confirming participant-reported outcomes. Of the 1417 individuals with the intention-to-treat sample, the remote nature of the study caused an additional 94 participants to not take any doses of study drug. Therefore, 1323 participants were in the modified intention-to-treat sample, which was the a priori primary study sample. Only 1.4% of participants were lost to follow-up. Decentralized strategies facilitated the successful completion of the COVID-OUT trial without any in-person contact by expediting intervention delivery, expanding trial access geographically, limiting contagion exposure, and making it easy for participants to complete follow-up visits. Remotely completed consent and follow-up facilitated enrollment.

An exploratory survey about using ChatGPT in education, healthcare, and research.

OBJECTIVE: ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare. MATERIALS AND METHODS: We surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with a Fisher's Exact. Furthermore, we used qualitative methods to analyze and code discussions. RESULTS: We received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare. DISCUSSION: There was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.

Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach.

BACKGROUND: Peripheral artery disease (PAD) is underdiagnosed due to poor patient and clinician awareness. Despite this, no widely accepted PAD screening is recommended. OBJECTIVES: The authors used machine learning to develop an automated risk stratification tool for identifying patients with a high likelihood of PAD. METHODS: Using data from the electronic health record (EHR), ankle-brachial indices (ABIs) were extracted for 3,298 patients. In addition to ABI, we extracted 60 other patient characteristics and used a random forest model to rank the features by association with ABI. The model identified several features independently correlated with PAD. We then built a logistic regression model to predict PAD status on a validation set of patients (n = 1,089), an external cohort of patients (n = 2,922), and a national database (n = 2,488). The model was compared to an age-based and random forest model. RESULTS: The model had an area under the curve (AUC) of 0.68 in the validation set. When evaluated on an external population using EHR data, it performed similarly with an AUC of 0.68. When evaluated on a national database, it had an AUC of 0.72. The model outperformed an age-based model (AUC: 0.62; P < 0.001). A random forest model with inclusion of all 60 features did not perform significantly better (AUC: 0.71; P = 0.31). CONCLUSIONS: Statistical techniques can be used to build models which identify individuals at high risk for PAD using information accessible from the EHR. Models such as this may allow large health care systems to efficiently identify patients that would benefit from aggressive preventive strategies or targeted-ABI screening.

Machine learning identification of patient clusters for cutaneous melanoma.

BACKGROUND: Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma. METHODS: A Bayesian Gaussian Mixture ML model was created with data from 2056 patients diagnosed with cutaneous melanoma and then used to group the patients into six Clusters based on a Silhouette Score analysis. A t-distributed stochastic neighbor embedding (t-SNE) model was used to visualize the six Clusters. RESULTS: Statistical analysis revealed that Cluster 4 showed a significantly higher rate of metastatic disease, as well as higher Breslow depth at diagnosis, compared to the other five Clusters. Compared to the other five Clusters, patients represented in Cluster 4 also had lower healthcare utilization, fewer dermatology clinic visits, fewer primary care providers, and less frequent colonoscopies and mammograms, and were more likely to smoke and less likely to have a prior diagnosis of basal cell carcinoma. CONCLUSIONS: This study uncovers gaps in healthcare utilization of services among patient groups with cutaneous melanoma as well as possible implications for management of disease progression. Data-driven analyses emphasize the importance of routine clinic visits to dermatologists and/or primary care physicians (PCPs) for early detection and management of cutaneous melanoma. The findings from this study demonstrate that unsupervised ML methodology may serve to define the best candidate patients to benefit from enhanced dermatology/primary care which, in turn, is expected to improve outcomes for cutaneous melanoma.

Metformin reduces SARS-CoV-2 in a Phase 3 Randomized Placebo Controlled Clinical Trial.

Current antiviral treatment options for SARS-CoV-2 infections are not available globally, cannot be used with many medications, and are limited to virus-specific targets.1-3 Biophysical modeling of SARS-CoV-2 replication predicted that protein translation is an especially attractive target for antiviral therapy.4 Literature review identified metformin, widely known as a treatment for diabetes, as a potential suppressor of protein translation via targeting of the host mTor pathway.5 In vitro, metformin has antiviral activity against RNA viruses including SARS-CoV-2.6,7 In the COVID-OUT phase 3, randomized, placebo-controlled trial of outpatient treatment of COVID-19, metformin had a 42% reduction in ER visits/hospitalizations/death through 14 days; a 58% reduction in hospitalizations/death through 28 days, and a 42% reduction in Long COVID through 10 months.8,9 Here we show viral load analysis of specimens collected in the COVID-OUT trial that the mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (-0.56 log10 copies/mL; 95%CI, -1.05 to -0.06, p=0.027) while there was no virologic effect for ivermectin or fluvoxamine vs placebo. The metformin effect was consistent across subgroups and with emerging data.10,11 Our results demonstrate, consistent with model predictions, that a safe, widely available,12 well-tolerated, and inexpensive oral medication, metformin, can be repurposed to significantly reduce SARS-CoV-2 viral load.

Outpatient treatment of COVID-19 and incidence of post-COVID-19 condition over 10 months (COVID-OUT): a multicentre, randomised, quadruple-blind, parallel-group, phase 3 trial.

BACKGROUND: Post-COVID-19 condition (also known as long COVID) is an emerging chronic illness potentially affecting millions of people. We aimed to evaluate whether outpatient COVID-19 treatment with metformin, ivermectin, or fluvoxamine soon after SARS-CoV-2 infection could reduce the risk of long COVID. METHODS: We conducted a decentralised, randomised, quadruple-blind, parallel-group, phase 3 trial (COVID-OUT) at six sites in the USA. We included adults aged 30-85 years with overweight or obesity who had COVID-19 symptoms for fewer than 7 days and a documented SARS-CoV-2 positive PCR or antigen test within 3 days before enrolment. Participants were randomly assigned via 2 × 3 parallel factorial randomisation (1:1:1:1:1:1) to receive metformin plus ivermectin, metformin plus fluvoxamine, metformin plus placebo, ivermectin plus placebo, fluvoxamine plus placebo, or placebo plus placebo. Participants, investigators, care providers, and outcomes assessors were masked to study group assignment. The primary outcome was severe COVID-19 by day 14, and those data have been published previously. Because the trial was delivered remotely nationwide, the a priori primary sample was a modified intention-to-treat sample, meaning that participants who did not receive any dose of study treatment were excluded. Long COVID diagnosis by a medical provider was a prespecified, long-term secondary outcome. This trial is complete and is registered with ClinicalTrials.gov, NCT04510194. FINDINGS: Between Dec 30, 2020, and Jan 28, 2022, 6602 people were assessed for eligibility and 1431 were enrolled and randomly assigned. Of 1323 participants who received a dose of study treatment and were included in the modified intention-to-treat population, 1126 consented for long-term follow-up and completed at least one survey after the assessment for long COVID at day 180 (564 received metformin and 562 received matched placebo; a subset of participants in the metformin vs placebo trial were also randomly assigned to receive ivermectin or fluvoxamine). 1074 (95%) of 1126 participants completed at least 9 months of follow-up. 632 (56·1%) of 1126 participants were female and 494 (43·9%) were male; 44 (7·0%) of 632 women were pregnant. The median age was 45 years (IQR 37-54) and median BMI was 29·8 kg/m2 (IQR 27·0-34·2). Overall, 93 (8·3%) of 1126 participants reported receipt of a long COVID diagnosis by day 300. The cumulative incidence of long COVID by day 300 was 6·3% (95% CI 4·2-8·2) in participants who received metformin and 10·4% (7·8-12·9) in those who received identical metformin placebo (hazard ratio [HR] 0·59, 95% CI 0·39-0·89; p=0·012). The metformin beneficial effect was consistent across prespecified subgroups. When metformin was started within 3 days of symptom onset, the HR was 0·37 (95% CI 0·15-0·95). There was no effect on cumulative incidence of long COVID with ivermectin (HR 0·99, 95% CI 0·59-1·64) or fluvoxamine (1·36, 0·78-2·34) compared with placebo. INTERPRETATION: Outpatient treatment with metformin reduced long COVID incidence by about 41%, with an absolute reduction of 4·1%, compared with placebo. Metformin has clinical benefits when used as outpatient treatment for COVID-19 and is globally available, low-cost, and safe. FUNDING: Parsemus Foundation; Rainwater Charitable Foundation; Fast Grants; UnitedHealth Group Foundation; National Institute of Diabetes, Digestive and Kidney Diseases; National Institutes of Health; and National Center for Advancing Translational Sciences.

An exploratory survey about using ChatGPT in education, healthcare, and research.

Objective ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare. Materials and Methods We surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with Fisher's Exact. Furthermore, we used qualitative methods to analyze and code discussions. Results We received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare. Discussion There was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.

Characteristics of patient navigation programs in the Cancer Moonshot ACCSIS colorectal cancer screening initiative.

BACKGROUND: Although patient navigation has shown promise for increasing participation in colorectal cancer screening and follow-up, little evidence is available to guide implementation of patient navigation in clinical practice. We characterize 8 patient navigation programs being implemented as part of multi-component interventions of the National Cancer Institute's Cancer Moonshot Accelerating Colorectal Cancer Screening and Follow-Up Through Implementation Science (ACCSIS) initiative. METHODS: We developed a data collection template organized by ACCSIS framework domains. The template was populated by a representative from each of the 8 ACCSIS research projects. We report standardized descriptions of 1) the socio-ecological context in which the navigation program was being conducted, 2) navigation program characteristics, 3) activities undertaken to facilitate program implementation (eg, training), and 4) outcomes used in program evaluation. RESULTS: ACCSIS patient navigation programs varied broadly in their socio-ecological context and settings, the populations they served, and how they were implemented in practice. Six research projects adapted and implemented evidence-based patient navigation programs; the remaining projects developed new programs. Five projects began navigation when patients were due for initial colorectal cancer screening; 3 projects began navigation later in the screening process, when patients were due for follow-up colonoscopy after an abnormal stool-test result. Seven projects relied on existing clinical staff to deliver the navigation; 1 hired a centralized research navigator. All project researchers plan to evaluate the effectiveness and implementation of their programs. CONCLUSIONS: Our detailed program descriptions may facilitate cross-project comparisons and guide future implementation and evaluation of patient navigation programs in clinical practice.

Impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccination and Booster on Coronavirus Disease 2019 (COVID-19) Symptom Severity Over Time in the COVID-OUT Trial.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination has decreasing protection from acquiring any infection with emergence of new variants; however, vaccination continues to protect against progression to severe coronavirus disease 2019 (COVID-19). The impact of vaccination status on symptoms over time is less clear. METHODS: Within a randomized trial on early outpatient COVID-19 therapy testing metformin, ivermectin, and/or fluvoxamine, participants recorded symptoms daily for 14 days. Participants were given a paper symptom diary allowing them to circle the severity of 14 symptoms as none (0), mild (1), moderate (2), or severe (3). This is a secondary analysis of clinical trial data on symptom severity over time using generalized estimating equations comparing those unvaccinated, SARS-CoV-2 vaccinated with primary vaccine series only, or vaccine-boosted. RESULTS: The parent clinical trial prospectively enrolled 1323 participants, of whom 1062 (80%) prospectively recorded some daily symptom data. Of these, 480 (45%) were unvaccinated, 530 (50%) were vaccinated with primary series only, and 52 (5%) vaccine-boosted. Overall symptom severity was least for the vaccine-boosted group and most severe for unvaccinated at baseline and over the 14 days (P < .001). Individual symptoms were least severe in the vaccine-boosted group including cough, chills, fever, nausea, fatigue, myalgia, headache, and diarrhea, as well as smell and taste abnormalities. Results were consistent over Delta and Omicron variant time periods. CONCLUSIONS: SARS-CoV-2 vaccine-boosted participants had the least severe symptoms during COVID-19, which abated the quickest over time. Clinical Trial Registration. NCT04510194.

Randomized Trial of Metformin, Ivermectin, and Fluvoxamine for Covid-19.

BACKGROUND: Early treatment to prevent severe coronavirus disease 2019 (Covid-19) is an important component of the comprehensive response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. METHODS: In this phase 3, double-blind, randomized, placebo-controlled trial, we used a 2-by-3 factorial design to test the effectiveness of three repurposed drugs - metformin, ivermectin, and fluvoxamine - in preventing serious SARS-CoV-2 infection in nonhospitalized adults who had been enrolled within 3 days after a confirmed diagnosis of infection and less than 7 days after the onset of symptoms. The patients were between the ages of 30 and 85 years, and all had either overweight or obesity. The primary composite end point was hypoxemia (≤93% oxygen saturation on home oximetry), emergency department visit, hospitalization, or death. All analyses used controls who had undergone concurrent randomization and were adjusted for SARS-CoV-2 vaccination and receipt of other trial medications. RESULTS: A total of 1431 patients underwent randomization; of these patients, 1323 were included in the primary analysis. The median age of the patients was 46 years; 56% were female (6% of whom were pregnant), and 52% had been vaccinated. The adjusted odds ratio for a primary event was 0.84 (95% confidence interval [CI], 0.66 to 1.09; P = 0.19) with metformin, 1.05 (95% CI, 0.76 to 1.45; P = 0.78) with ivermectin, and 0.94 (95% CI, 0.66 to 1.36; P = 0.75) with fluvoxamine. In prespecified secondary analyses, the adjusted odds ratio for emergency department visit, hospitalization, or death was 0.58 (95% CI, 0.35 to 0.94) with metformin, 1.39 (95% CI, 0.72 to 2.69) with ivermectin, and 1.17 (95% CI, 0.57 to 2.40) with fluvoxamine. The adjusted odds ratio for hospitalization or death was 0.47 (95% CI, 0.20 to 1.11) with metformin, 0.73 (95% CI, 0.19 to 2.77) with ivermectin, and 1.11 (95% CI, 0.33 to 3.76) with fluvoxamine. CONCLUSIONS: None of the three medications that were evaluated prevented the occurrence of hypoxemia, an emergency department visit, hospitalization, or death associated with Covid-19. (Funded by the Parsemus Foundation and others; COVID-OUT ClinicalTrials.gov number, NCT04510194.).

Vaccination Against SARS-CoV-2 Is Associated With a Lower Viral Load and Likelihood of Systemic Symptoms.

Background: Data conflict on whether vaccination decreases severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load. The objective of this analysis was to compare baseline viral load and symptoms between vaccinated and unvaccinated adults enrolled in a randomized trial of outpatient coronavirus disease 2019 (COVID-19) treatment. Methods: Baseline data from the first 433 sequential participants enrolling into the COVID-OUT trial were analyzed. Adults aged 30-85 with a body mass index (BMI) ≥25 kg/m2 were eligible within 3 days of a positive SARS-CoV-2 test and <7 days of symptoms. Log10 polymerase chain reaction viral loads were normalized to human RNase P by vaccination status, by time from vaccination, and by symptoms. Results: Two hundred seventy-four participants with known vaccination status contributed optional nasal swabs for viral load measurement: median age, 46 years; median (interquartile range) BMI 31.2 (27.4-36.4) kg/m2. Overall, 159 (58%) were women, and 217 (80%) were White. The mean relative log10 viral load for those vaccinated <6 months from the date of enrollment was 0.11 (95% CI, -0.48 to 0.71), which was significantly lower than the unvaccinated group (P = .01). Those vaccinated ≥6 months before enrollment did not differ from the unvaccinated with respect to viral load (mean, 0.99; 95% CI, -0.41 to 2.40; P = .85). The vaccinated group had fewer moderate/severe symptoms of subjective fever, chills, myalgias, nausea, and diarrhea (all P < .05). Conclusions: These data suggest that vaccination within 6 months of infection is associated with a lower viral load, and vaccination was associated with a lower likelihood of having systemic symptoms.

Factors That Influence Clinician Experience with Electronic Health Records.

Objectives: To report quantitative and qualitative analyses of features, functionalities, organizational, training, clinical specialties, and other factors that impact electronic health record (EHR) experience based on a survey by two large healthcare systems. Materials and Methods: A total of 816 clinicians-352 (43 percent) physicians, 96 (12 percent) residents/fellows, 177 (22 percent) nurses, 96 (12 percent) advanced practice providers, and 95 (12 percent) allied health professionals-completed surveys on different EHRs. Responses were analyzed for quantitative and qualitative factors. The measured outcome was calculated as a net EHR experience. Results: Net EHR experience represents overall satisfaction that clinicians report with the EHR and its usability. EHR experience for Virginia Commonwealth University Medical Center and University of Chicago Medicine was low. There were noticeable differences in physician and nursing experiences with EHRs at both universities. EHR personalization, years of practice, impact on efficiency, quality of care, and satisfaction with EHR training contributed significantly to the net EHR experience. Satisfaction of certain specialty practitioners such as endocrinology, family medicine, infectious disease, nephrology, neurology, and pulmonology was noted to be especially low. Ability to use a split-screen function to view labs, follow-up training from other providers rather than vendors, reduced documentation time burden, fewer click boxes, more customizable order sets, improved messaging, e-prescribing, and improved integration were the most common desired EHR improvements requested on qualitative analysis. Discussion: EHR experience was low regardless of the system and may be improved by better EHR training, increased utilization of personalization tools, reduced documentation burden, and enhanced EHR design and functionality. There was a difference between provider and nursing experiences with the EHR. Conclusion: Designing better EHR training, increasing utilization of personalization tools, enhancing functionality, and decreasing documentation burden may lead to a better EHR experience.

Outpatient treatment of Covid-19 with metformin, ivermectin, and fluvoxamine and the development of Long Covid over 10-month follow-up.

Background: Long Covid is an emerging chronic illness potentially affecting millions, sometimes preventing the ability to work or participate in normal daily activities. COVID-OUT was an investigator-initiated, multi-site, phase 3, randomized, quadruple-blinded placebo-controlled clinical trial (NCT04510194). The design simultaneously assessed three oral medications (metformin, ivermectin, fluvoxamine) using two by three parallel treatment factorial assignment to efficiently share placebo controls and assessed Long Covid outcomes for 10 months to understand whether early outpatient treatment of SARS-CoV-2 with metformin, ivermectin, or fluvoxamine prevents Long Covid. Methods: This was a decentralized, remotely delivered trial in the US of 1,125 adults age 30 to 85 with overweight or obesity, fewer than 7 days of symptoms, and enrolled within three days of a documented SARS-CoV-2 infection. Immediate release metformin titrated over 6 days to 1,500mg per day 14 days total; ivermectin 430mcg/kg/day for 3 days; fluvoxamine, 50mg on day one then 50mg twice daily through 14 days. Medical-provider diagnosis of Long Covid, reported by participant by day 300 after randomization was a pre-specified secondary outcome; the primary outcome of the trial was severe Covid by day 14. Result: The median age was 45 years (IQR 37 to 54), 56% female of whom 7% were pregnant. Two percent identified as Native American; 3.7% as Asian; 7.4% as Black/African American; 82.8% as white; and 12.7% as Hispanic/Latino. The median BMI was 29.8 kg/m2 (IQR 27 to 34); 51% had a BMI >30kg/m2. Overall, 8.4% reported having received a diagnosis of Long Covid from a medical provider: 6.3% in the metformin group and 10.6% in the metformin control; 8.0% in the ivermectin group and 8.1% in the ivermectin control; and 10.1% in the fluvoxamine group and 7.5% in the fluvoxamine control. The Hazard Ratio (HR) for Long Covid in the metformin group versus control was 0.58 (95% CI 0.38 to 0.88); 0.99 (95% CI 0.592 to 1.643) in the ivermectin group; and 1.36 in the fluvoxamine group (95% CI 0.785 to 2.385). Conclusions: There was a 42% relative decrease in the incidence of Long Covid in the metformin group compared to its blinded control in a secondary outcome of this randomized phase 3 trial.

Indication alerts to improve problem list documentation.

BACKGROUND: Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. METHODS: We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. RESULTS: Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%. CONCLUSIONS: Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

Identifying actionable strategies: using Consolidated Framework for Implementation Research (CFIR)-informed interviews to evaluate the implementation of a multilevel intervention to improve colorectal cancer screening.

BACKGROUND: Many evidence-based interventions (EBIs) found to be effective in research studies often fail to translate into meaningful patient outcomes in practice. The purpose of this study was to identify facilitators and barriers that affect the implementation of three EBIs to improve colorectal cancer (CRC) screening in an urban federally qualified health center (FQHC) and offer actionable recommendations to improve future implementation efforts. METHODS: We conducted 16 semi-structured interviews guided by the Consolidation Framework for Implementation Research (CFIR) to describe diverse stakeholders' implementation experience. The interviews were conducted in the participant's clinic, audio-taped, and professionally transcribed for analysis. RESULTS: We used the five CFIR domains and 39 constructs and subconstructs as a coding template to conduct a template analysis. Based on experiences with the implementation of three EBIs, stakeholders described barriers and facilitators related to the intervention characteristics, outer setting, and inner setting. Implementation barriers included (1) perceived burden and provider fatigue with EHR (Electronic Health Record) provider reminders, (2) unreliable and ineffectual EHR provider reminders, (3) challenges to providing health care services to diverse patient populations, (4) lack of awareness about CRC screening among patients, (5) absence of CRC screening goals, (6) poor communication on goals and performance, and (7) absence of printed materials for frontline implementers to educate patients. Implementation facilitators included (1) quarterly provider assessment and feedback reports provided real-time data to motivate change, (2) integration with workflow processes, (3) pressure from funding requirement to report quality measures, (4) peer pressure to achieve high performance, and (5) a culture of teamwork and patient-centered mentality. CONCLUSIONS: The CFIR can be used to conduct a post-implementation formative evaluation to identify barriers and facilitators that influenced the implementation. Furthermore, the CFIR can provide a template to organize research data and synthesize findings. With its clear terminology and meta-theoretical framework, the CFIR has the potential to promote knowledge-building for implementation. By identifying the contextual determinants, we can then determine implementation strategies to facilitate adoption and move EBIs to daily practice.

Outpatient metformin use is associated with reduced severity of COVID-19 disease in adults with overweight or obesity.

Observational studies suggest outpatient metformin use is associated with reduced mortality from coronavirus disease-2019 (COVID-19). Metformin is known to decrease interleukin-6 and tumor-necrosis factor-α, which appear to contribute to morbidity in COVID-19. We sought to understand whether outpatient metformin use was associated with reduced odds of severe COVID-19 disease in a large US healthcare data set. Retrospective cohort analysis of electronic health record (EHR) data that was pooled across multiple EHR systems from 12 hospitals and 60 primary care clinics in the Midwest between March 4, 2020 and December 4, 2020. Inclusion criteria: data for body mass index (BMI) > 25 kg/m2 and a positive SARS-CoV-2 polymerase chain reaction test; age ≥ 30 and ≤85 years. Exclusion criteria: patient opt-out of research. Metformin is the exposure of interest, and death, admission, and intensive care unit admission are the outcomes of interest. Metformin was associated with a decrease in mortality from COVID-19, OR 0.32 (0.15, 0.66; p = .002), and in the propensity-matched cohorts, OR 0.38 (0.16, 0.91; p = .030). Metformin was associated with a nonsignificant decrease in hospital admission for COVID-19 in the overall cohort, OR 0.78 (0.58-1.04, p = .087). Among the subgroup with a hemoglobin HbA1c available (n = 1193), the adjusted odds of hospitalization (including adjustment for HbA1c) for metformin users was OR 0.75 (0.53-1.06, p = .105). Outpatient metformin use was associated with lower mortality and a trend towards decreased admission for COVID-19. Given metformin's low cost, established safety, and the mounting evidence of reduced severity of COVID-19 disease, metformin should be prospectively assessed for outpatient treatment of COVID-19.

Implementing a multilevel intervention to accelerate colorectal cancer screening and follow-up in federally qualified health centers using a stepped wedge design: a study protocol.

BACKGROUND: Screening for colorectal cancer (CRC) not only detects disease early when treatment is more effective but also prevents cancer by finding and removing precancerous polyps. Because many of our nation's most disadvantaged and vulnerable individuals obtain health care at federally qualified health centers, these centers play a significant role in increasing CRC screening among the most vulnerable populations. Furthermore, the full benefits of cancer screenings must include timely and appropriate follow-up of abnormal results. Thus, the purpose of this study is to implement a multilevel intervention to increase rates of CRC screening, follow-up, and referral-to-care in federally qualified health centers, as well as simultaneously to observe and to gather information on the implementation process to improve the adoption, implementation, and sustainment of the intervention. The multilevel intervention will target three different levels of influences: organization, provider, and individual. It will have multiple components, including provider and staff education, provider reminder, provider assessment and feedback, patient reminder, and patient navigation. METHODS: This study is a multilevel, three-phase, stepped wedge cluster randomized trial with four clusters of clinics from four different FQHC systems. In the first phase, there will be a 3-month waiting period during which no intervention components will be implemented. After the 3-month waiting period, we will randomize two clusters to cross from the control to the intervention and the remaining two clusters to follow 3 months later. All clusters will stay at the same phase for 9 months, followed by a 3-month transition period, and then cross over to the next phase. DISCUSSION: There is a pressing need to reduce disparities in CRC outcomes, especially among racial/ethnic minority populations and among populations who live in poverty. Single-level interventions are often insufficient to lead to sustainable changes. Multilevel interventions, which target two or more levels of changes, are needed to address multilevel contextual influences simultaneously. Multilevel interventions with multiple components will affect not only the desired outcomes but also each other. How to take advantage of multilevel interventions and how to implement such interventions and evaluate their effectiveness are the ultimate goals of this study. TRIAL REGISTRATION: This protocol is registered at clinicaltrials.gov ( NCT04514341 ) on 14 August 2020.